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| 长江中游粮食主产区农作物生产碳公平时空演变与驱动因素 |
| Spatiotemporal evolution and driving factors of carbon equity in crop production in the main grain producing areas of the middle reaches of the Yangtze River |
| Received:March 05, 2025 |
| DOI：10.13254/j.jare.2025.0160 |
| 中文关键词: 农作物生产，碳公平，驱动因素，XGBoost模型，长江中游粮食主产区 |
| 英文关键词: crop production, carbon equity, driving factor, XGBoost model, main grain producing areas in the middle reaches of the Yangtze River |
| 基金项目:国家自然科学基金项目（42261049）；江西省自然科学基金项目（20232BAB203061）；江西省教改课题一般项目（JXJG-24-4-27）；江西财经大学第十九届学生科研课题（20241129114437796） |
| Author Name | Affiliation | E-mail | | Lü Tiangui | School of Public Administration, Jiangxi University of Finance and Economics, Nanchang 330013, China | | | YUAN Menghan | School of Public Administration, Jiangxi University of Finance and Economics, Nanchang 330013, China | | | HUANG Xianzhe | School of Public Administration, Jiangxi University of Finance and Economics, Nanchang 330013, China | | | YANG Daotian | School of Public Administration, Jiangxi University of Finance and Economics, Nanchang 330013, China | yangdaotian@126.com | | FU Shufei | School of Digital Economics, Jiangxi University of Finance and Economics, Nanchang 330013, China | | | CHEN Anying | School of Public Administration, Jiangxi University of Finance and Economics, Nanchang 330013, China | |
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| 中文摘要: |
| 为识别粮食主产区农作物生产碳公平时空特征及驱动因素，本研究基于碳源-碳汇测度2008—2023年长江中游粮食主产区农作物生产碳公平系数，运用XGBoost机器学习模型识别碳公平的关键驱动因素。结果表明：2008—2023年，长江中游粮食主产区的碳公平系数总体大于1，但存在一定波动，碳公平水平增长率高、低峰值分别为1.351%、-0.665%；各省份碳公平水平差异显著，江西省最高，湖南省次之，湖北省最低；碳公平性在空间上呈现显著异质性，高值区片状连续分布，低值区点状分散，部分地市长期处于低值状态；气温是影响研究区碳公平性的核心变量，其次是城镇化水平与农业劳动力投入。研究表明，通过制定区域差异化政策支持策略、优化城镇化与农业绿色转型协同推进以及建立健全碳公平监测与评估体系，可全面提升区域碳公平水平，推动农业可持续发展与低碳转型。 |
| 英文摘要: |
| To identify the spatiotemporal characteristics and driving factors of carbon equity in crop production in major grain-producing areas, this study analyzed the carbon equity coefficient of crop production in the middle Yangtze River grain-producing region from 2008 to 2023 based on carbon source-sink assessment. The XGBoost machine learning model was used to identify the key driving factors of carbon equity. The results revealed that from 2008 to 2023, the carbon equity coefficient in the middle Yangtze River grain-producing region was generally greater than 1, but it exhibited fluctuations, with peak growth rates of 1.351%（highest）and-0.665%（lowest）, respectively. Significant differences in carbon equity levels were observed among provinces, with Jiangxi Province having the highest level, Hunan Province ranking second, and Hubei Province having the lowest. Carbon equity exhibited significant spatial heterogeneity, with high-value areas distributed continuously and low-value areas scattered sporadically, with some cities remaining in a low-value state over the long term. Temperature was identified as the core variable influencing carbon equity in the study area, followed by urbanization levels and agricultural labor input. Studies show that by formulating regional differentiated policy support strategies, optimizing the synergistic promotion of urbanization and agricultural green transformation, and establishing and improving a carbon equity monitoring and evaluation system, the regional carbon equity level can be comprehensively improved, thereby promoting sustainable agricultural development and low-carbon transition. |
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